TY - GEN
T1 - A new manifold distance measure for visual object categorization
AU - Li, Fengfu
AU - Huang, Xiayuan
AU - Qiao, Hong
AU - Zhang, Bo
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/9/27
Y1 - 2016/9/27
N2 - Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level comparison and thus easily affected by image rotations and translations. In this paper, we propose a new manifold distance to model the dissimilarities between visual objects based on the Complex Wavelet Structural Similarity (CW-SSIM) index. The proposed distance is more robust to rotations and translations of images than the traditional manifold distance and the CW-SSIM index based distance. In addition, the proposed distance is combined with the k-medoids clustering method to derive a new clustering method for visual object categorization. Experiments on Coil-20, Coil-100 and Olivetti Face Databases show that the proposed distance measure is better for visual object categorization than both the traditional manifold distances and the CW-SSIM index based distances.
AB - Manifold distances are very effective tools for visual object recognition. However, most of the traditional manifold distances between images are based on the pixel-level comparison and thus easily affected by image rotations and translations. In this paper, we propose a new manifold distance to model the dissimilarities between visual objects based on the Complex Wavelet Structural Similarity (CW-SSIM) index. The proposed distance is more robust to rotations and translations of images than the traditional manifold distance and the CW-SSIM index based distance. In addition, the proposed distance is combined with the k-medoids clustering method to derive a new clustering method for visual object categorization. Experiments on Coil-20, Coil-100 and Olivetti Face Databases show that the proposed distance measure is better for visual object categorization than both the traditional manifold distances and the CW-SSIM index based distances.
UR - https://www.scopus.com/pages/publications/84991738109
U2 - 10.1109/WCICA.2016.7578645
DO - 10.1109/WCICA.2016.7578645
M3 - 会议稿件
AN - SCOPUS:84991738109
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 2232
EP - 2236
BT - Proceedings of the 2016 12th World Congress on Intelligent Control and Automation, WCICA 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th World Congress on Intelligent Control and Automation, WCICA 2016
Y2 - 12 June 2016 through 15 June 2016
ER -